Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR).

ABSTRACT

An implementation of a technology, described herein, for relevance-feedback, content-based facilitating accurate and efficient image retrieval minimizes the number of iterations for user feedback regarding the semantic relevance of exemplary images while maximizing the resulting relevance of each iteration. One technique for accomplishing this is to use a Bayesian classifier to treat positive and negative feedback examples with different strategies. In addition, query refinement techniques are applied to pinpoint the users&#39; intended queries with respect to their feedbacks. These techniques further enhance the accuracy and usability of relevance feedback. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/832,501, filed Apr. 26, 2004, which is a continuation of U.S. patentapplication Ser. No. 09/823,534, filed Mar. 30, 2001, which issued onJun. 8, 2004 as U.S. Pat. No. 6,748,398. This application claimspriority to U.S. patent application Ser. No. 09/823,534, filed Mar. 30,2001. These patent application Ser. Nos. 10/832,501 and 09/823,534 areincorporated herein by reference.

TECHNICAL FIELD

This invention generally relates to a technology facilitating accurateand efficient image retrieval.

BACKGROUND

Digital images are increasingly more common as scanners and digitalcameras drop in price and increase in availability and function. Asdigital photographers (amateurs and professionals alike) amass largecollections of digital photographs on their computers, the challengesinvolved with organizing, querying, and accessing digital images grow.

Therefore, digital photographers need to utilize “image retrieval”technology to accomplish their tasks. “Image retrieval” refers to atechnology focused on the organization of a library of digital images,the inquiry into such a library, and the retrieval of selected imagesthat meet the terms of such inquiry.

Images in a library may be organized and, thus, retrieved in anorganized fashion based upon their content. Content-based categorizationand image retrieval approaches are beneficial to all those with accessto a library of digital images.

Image Retrieval Systems

Automatic image retrieval systems provide an efficient way for users tonavigate through the growing numbers of available images. Traditionalimage retrieval systems allow users to retrieve images in one of twoways: (1) keyword-based image retrieval or (2) content-based imageretrieval.

Keyword-Based. Keyword-based image retrieval finds images by matchingkeywords from a user query to keywords that have been manually added tothe images. Thus, these images have been manually annotated withkeywords related to their semantic content. One of the more popularcollections of annotated images is “Corel™ Gallery”, an image databasefrom Corel Corporation that includes upwards of one million annotatedimages.

Unfortunately, with keyword-based image retrieval systems, it can bedifficult or impossible for a user to precisely describe the inherentcomplexity of certain images. As a result, retrieval accuracy can beseverely limited because some images—those that cannot be described orcan only be described ambiguously—will not be retrieved successfully. Inaddition, due to the enormous burden of manual annotation, there are alimited number of databases with annotated images.

Although image retrieval techniques based on keywords can be easilyautomated, they suffer from the same problems as the informationretrieval systems in text databases and web-based search engines.Because of wide spread synonymy and polysemy in natural language, theprecision of such systems is very low and their recall is inadequate.(Synonymy is the quality of being synonymous; equivalence of meaning.Polysemy means having or characterized by many meanings.) In addition,linguistic barriers and the lack of uniform textual descriptions forcommon image attributes severely limit the applicability of the keywordbased systems.

Content-Based. Content-based image retrieval (CBIR) systems have beenbuilt to address many issues, such as those of keyword-based systems.These systems extract visual image features such as color, texture, andshape from the image collections and utilize them for retrievalpurposes. These visual image features are also called “low-level”features. Examples of low-level features of an image include colorhistogram, wavelet based texture descriptors, directional histograms ofedges, and so forth.

CBIR systems work well when the extracted feature vectors accuratelycapture the essence of the image content. For example, if a user issearching for an image with complex textures having a particularcombination of colors, this type of query is extremely difficult todescribe using keywords, but it can be reasonably represented by acombination of color and texture features. On the other hand, if a useris searching for an object that has clear semantic meanings but cannotbe sufficiently represented by combinations of available featurevectors, the content-based systems will not return many relevantresults. Furthermore, the inherent complexity of the images makes italmost impossible for users to present the system with a query thatfully describes the their intentions.

Although CBIR solves many of the problems of keyword-based imageretrieval, it has its own shortcomings. One such shortcoming is thatsearches may return entirely irrelevant images that just happen topossess similar features. Additionally, individual objects in imagescontain a wide variety of low-level features. Therefore, using only thelow-level features will not satisfactorily describe what is to beretrieved.

Semantic Concepts. The user is typically looking for specific semanticconcepts rather than specific low-level features. However, there is adisparity between “semantic concepts” and “low-level image features.”This disparity limits the performance of CBIR systems. Semantic conceptsinclude meaningful content of an image—for example, a river, a person, acar, a boat, etc. Although objectively measurable, low-level imagefeatures lack specific meaning.

The mapping between semantic concepts and low-level features is stillimpractical with present computer vision and AI techniques. To improvethis situation, more research efforts have been shifted to “relevancefeedback” techniques recently.

Relevance-Feedback CBIR

A common type of a CBIR system is one that finds images that are similarto low-level features of an example image or example images. To weed outthe irrelevant images returned in CBIR, some CBIR systems utilize userfeedback to gain an understanding as to the relevancy of certain images.The user feedback is in the form of selected exemplary images (eitherpositive or negative). These exemplary images may be called “feedback”images.

The user feedback selects the exemplary images used to narrow successivesearches. A common approach to relevance feedback is estimating idealquery parameters using the low-level image features of the exemplaryimages. Thus, relevance feedback maps low-level features to humanrecognition of semantic concepts.

In a relevance-feedback CBIR system, a user submits a query and thesystem provides a set of query results. More specifically, after aquery, the system presents a set of images to the human querier. Thehuman designates specific images as positive or negative. Positiveindicates that the image contains the semantic concepts queried andnegative indicates that the image does not contain such concepts.

Based upon this feedback, the system performs a new query and displays anew set of resulting images. The human again provides feedback regardingthe relevance of the displayed images. Another round of query andfeedback is performed. Each round may be called an iteration. Theprocess continues for a given number of iterations or until the user (orsystem) is satisfied with the overall relevance of the present set ofimages.

One of the most popular models used in information retrieval is thevector model. The vector model is described in such writings as Buckleyand Salton, “Optimization of Relevance Feedback Weights,” in Proc ofSIGIR'95; Salton and McGill, “Introduction to Modern InformationRetrieval,” McGraw-Hill Book Company, 1983; and W. M. Shaw,“Term-Relevance Computation and Perfect Retrieval Performance,”Information processing and Management. Various effective retrievaltechniques have been developed for this model and among them is themethod of relevance feedback.

Most of the existing relevance feedback research can be classified intotwo approaches: query point movement and re-weighting.

Query-Point-Movement

The query-point-movement method essentially tries to improve theestimate of an “ideal query point” by moving it towards good examplepoints and away from bad example points. The frequently used techniqueto iteratively improve this estimation is the Rocchio's equation givenbelow for sets of relevant documents D′_(R) and non-relevant documentsD′_(N) noted by the user:

$\begin{matrix}{Q^{\prime} = {{\alpha\; Q} + {\beta( {\frac{1}{N_{R^{\prime}}}{\sum\limits_{i \in D_{R}^{\prime}}^{\;}\; D_{i}}} )} - {\gamma( {\frac{1}{N_{N^{\prime}}}{\sum\limits_{i \in D_{N}^{\prime}}^{\;}\; D_{i}}} )}}} & (1)\end{matrix}$where α, β, and γ are suitable constants and N_(R′) and N_(N′) are thenumber of documents in D′_(R) and D′_(N) respectively. In this equation,D′_(R) are those images (i.e., documents) that the user found relevantand D′_(N) are those images that the user did not find relevant.

The first portion (before the subtraction sign) of Equation 1 is a“reward function” that rewards query results that include the desiredsemantic content. The reward is based upon the positive feedback fromthe querier. The last portion (after the subtraction sign) of Equation 1is a “penalty function” that penalizes query results that do not includethe desired semantic content. The penalty is based upon the negativefeedback from the querier.

This technique is employed, for example, by the MARS system, asdescribed in Rui, Y, Huang, T. S., and Mehrotra, S. “Content-Based ImageRetrieval with Relevance Feedback in MARS,” in Proc. IEEE Int. Conf. onImage proc., 1997.

Some existing implementations of point movement strategy use a Bayesianmethod. Specifically, these include Cox et al. (Cox, I. J., Miller, M.L., Minka, T.P., Papathornas, T. V., Yianilos, P. N. “The Bayesian ImageRetrieval System, PicHunter: Theory, Implementation, and PsychophysicalExperiments” IEEE Tran. On Image Processing, Volume 9, Issue 1, pp.20–37, January 2000) and Vasconcelos and Lippman (Vasconcelos, N., andLippman, A., “A Bayesian Framework for Content-Based Indexing andRetrieval”, In: Proc. of DCC'98, Snowbird, Utah, 1998) used Bayesianlearning to incorporate user's feedback to update the probabilitydistribution of all the images in the database.

In these conventional works, they consider the feedback examples to thesame query to be independent with each other. They do this so that theycan use Naive Bayesian Inference to optimize the retrieval results byusing feedback examples.

These conventional works do not treat all positive examples to beclosely is related with each other. They do not use all these positiveexamples of the same query to construct a Bayesian classifier and usethat classifier to represent the original query and try to get moreaccurate retrieval results. These works are not incremental.

Re-Weighting

With the re-weighting method, each image is represented by an Ndimensional feature vector; thus, the image may be viewed as a point inan N dimensional space. Therefore, if the variance of the good examplesis high along a principle axis j, the values on this axis are mostlikely not very relevant to the input query and a low weight w_(j) canbe assigned to the axis. Therefore, the inverse of the standarddeviation of the j^(th) feature values in the feature matrix is used asthe basic idea to update the weight w_(j). The MARS system mentionedabove implements a slight refinement to the re-weighting method calledthe standard deviation method.

To optimize the query for further image similarity assessment,conventional relevance-feedback systems use only weighted feature sum(WFS) of the feedback images. WFS is a conventional query-refinementtechnique. WFS requires many iterations (well more than three) toproduce adequate results. WFS does not work very well in many cases,particularly when the user wants to express an “OR” relationship amongthe queries.

Multiple Iterations

Conventional relevance feedback techniques may require many iterationsbefore the majority of these results include images with the desiredsemantic content. They require at least three iterations, but typicallymuch more than three iterations, before generating results with thedesired semantic content.

These conventional relevance feedback methods either have no strategy toprogressively adjust their results or have bad performances on largedatasets. With conventional relevance feedback methods, the positive andnegative feedbacks are always treated as the same processes.

SUMMARY

Described herein is a technology for relevance-feedback, content-basedfacilitating accurate and efficient image retrieval. More specifically,the technology minimizes the number of iterations for user feedbackregarding the semantic relevance of exemplary images while maximizingthe resulting relevance of each iteration.

One technique for accomplishing this is to use a Bayesian classifier totreat positive and negative feedback examples with different strategies.A Bayesian classifier determines the distribution of the query space forpositive examples. Images near the negative examples are penalized usinga ‘dibbling’ process. This technique utilizes past feedback informationfor each iteration to progressively improve results.

In addition, query refinement techniques are applied to pinpoint theusers' intended queries with respect to their feedbacks. Thesetechniques further enhance the accuracy and usability of relevancefeedback.

This summary itself is not intended to limit the scope of this patent.Moreover, the title of this patent is not intended to limit the scope ofthis patent. For a better understanding of the present invention, pleasesee the following detailed description and appending claims, taken inconjunction with the accompanying drawings. The scope of the presentinvention is pointed out in the appending claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The same numbers are used throughout the drawings to reference likeelements and features.

FIG. 1 is a block diagram of an exemplary computer network in which aserver computer implements an image retrieval system (in accordance withan implementation of the claimed invention) that may be accessed over anetwork by one or more client computers.

FIG. 2 is a block diagram of an image retrieval system architecture inaccordance with an implementation of the claimed invention.

FIG. 3 is a flow diagram showing a methodological implementation of theinvention claimed herein.

FIG. 4 is a flow diagram showing a methodological implementation of theinvention claimed herein.

FIG. 5 is a flow diagram showing a methodological implementation of theinvention claimed herein.

FIG. 6 is an example of a computing environment capable of implementingan implementation (wholly or partially) of the invention claimed herein.

DETAILED DESCRIPTION

The following description sets forth specific embodiments of a relevancemaximizing, iteration minimizing, relevance-feedback, content-basedimage is retrieval (CBIR) that incorporate elements recited in theappended claims. These embodiments are described with specificity inorder to meet statutory written description, enablement, and best-moderequirements. However, the description itself is not intended to limitthe scope of this patent.

Described herein are one or more exemplary implementations of arelevance maximizing, iteration minimizing, relevance-feedback,content-based image retrieval (CBIR). The inventors intend theseexemplary implementations to be examples. The inventors do not intendthese exemplary implementations to limit the scope of the claimedpresent invention. Rather, the inventors have contemplated that theclaimed present invention might also be embodied and implemented inother ways, in conjunction with other present or future technologies.

An example of an embodiment of a relevance maximizing, iterationminimizing, relevance-feedback, content-based image retrieval (CBIR) maybe referred to as an “exemplary RFCBIR.”

Incorporation by Reference

The following co-pending patent applications, filed on Oct. 30, 2000,and assigned to the Microsoft Corporation, are incorporated by referenceherein:

-   -   U.S. patent application Ser. No. 09/702,292, entitled “Image        Retrieval Systems and Methods with Semantic and Feature Based        Relevance Feedback”;    -   U.S. patent application Ser. No. 09/702,288, entitled        “Semi-Automatic Annotation of Multimedia Objects”.

Introduction

The one or more exemplary implementations, described herein, of thepresent claimed invention may be implemented (whole or in part) by aRFCBIR system and/or by a computing environment like that shown in FIG.1 or 6.

The exemplary RFCBIR is a relevance-feedback CBIR system that minimizesquery-results iterations and maximizes the resulting relevancy of theresults of each iteration.

One implementation of the exemplary RFCBIR employs a newrelevance-feedback approach based on Bayesian classifier and it treatspositive and negative feedback examples with different strategies. Forpositive examples (i.e., images that include desired semantic content asdetermined by the relevance feedback of a human), a Bayesian classifieris used to determine the distribution of the query space. For negativeexamples, a ‘dibbling’ process is applied to penalize images that arenear the negative examples in the query and retrieval refinementprocess. This implementation has a progressive learning capability thatutilize past feedback information to help the current query.

Other implementations of the exemplary RFCBIR employ at least one ofthree query-refinement techniques (or a combination of such techniques)to evaluate the similarity between images in database and feedbackimages. Each of the three query-refinement techniques gives betterresults than the conventional WFS technique. The three techniquesdescribed herein include: weighted distance sum (WDS), minimal distance(MD), and minimum distance rank (MDR). Experimental comparisons showthat the MDR technique gives the best results in multiple images querysystem. These techniques may be combined.

Herein, the architecture of the exemplary RFCBIR is described in thecontext of an Internet-based system in which a server hosts the imageretrieval system and clients submit user queries to the server. However,the architecture may be implemented in other environments. For instance,the image retrieval architecture may be implemented innon-Internet-based client-server systems or on a non-networked computersystem.

Exemplary Computing Environment

FIG. 1 shows an exemplary computer network system 100 in which theRFCBIR system may be implemented. The network system 100 includes aclient computer 102 that submits queries to a server computer 104 via anetwork 106, such as the Internet. While the RFCBIR system can beimplemented using other networks (e.g., a wide area network or localarea network) and should not be limited to the Internet, the RFCBIRsystem will be described in the context of the Internet as one suitableimplementation. The web-based retrieval system allows multiple users toperform retrieval tasks simultaneously at any given time.

The client 102 is representative of many diverse computer systems,including general-purpose computers (e.g., desktop computer, laptopcomputer, etc.), network appliances (e.g., set-top box (STB), gameconsole, etc.), and the like. The client 102 includes a processor 110, avolatile memory 112 (e.g., RAM), and a non-volatile memory 114 (e.g.,ROM, Flash, hard disk, optical, etc.). The client 102 also has one ormore input devices 116 (e.g., keyboard, keypad, mouse, remote control,stylus, microphone, etc.) and a display 118 to display images returnedfrom the image retrieval system.

The client 102 is equipped with a browser 120, which is stored innon-volatile memory 114 and executed on processor 110. The browser 120submits requests to and receives responses from the server 104 via thenetwork 106. For discussion purposes, the browser 120 may be configuredas a conventional Internet browser that is capable of receiving andrendering documents written in a markup language, such as HTML(hypertext markup language). The browser may further be used to presentthe images on the display 118.

The server 104 is representative of many different server environments,including a server for a local area network or wide area network, abackend for such a server, or a Web server. In this latter environmentof a Web server, the server 104 may be implemented as one or morecomputers that are configured with server software to host a site on theInternet 106, such as a Web site for searching.

The server 104 has a processor 130, volatile memory 132 (e.g., RAM), andnon-volatile memory 134 (e.g., ROM, Flash, hard disk, optical, RAIDmemory, etc.). The server 104 runs an operating system 136 and an imageretrieval system 140. For purposes of illustration, operating system 136and image retrieval system 140 are illustrated as discrete blocks storedin the non-volatile memory 134, although it is recognized that suchprograms and components reside at various times in different storagecomponents of the server 104 and are executed by the processor 130.Generally, these software components are stored in non-volatile memory134 and from there, are loaded at least partially into the volatile mainmemory 132 for execution on the processor 130.

The image retrieval system 140 searches for images stored in imagedatabase 142. The image retrieval system 140 includes a query handler(not is shown), a feature and semantic matcher 152, and a feedbackanalyzer 154.

Typically, the query handler handles the initial queries received fromthe client 102. Such initial queries may be in the form of naturallanguage queries, individual word queries, or image queries thatcontains low-level features of an example image that forms the basis ofthe search. Depending on the query type, the query handler initiates afeature-based search of the image database 142. After the initial query,user feedback is available; therefore, the query handler is notnecessary after the initial query.

The feature and semantic matcher 152 searches for images in imagedatabase 142 that contain low-level features resembling the exampleimage(s). The feature and semantic matcher 152 rank the images accordingto their relevance to the query and return the images in rank order forreview by the user. Via a user interface, the user can mark or otherwiseidentify individual images as more relevant to the query or as less ornot relevant to the query.

The feedback analyzer 154 monitors the user feedback and analyzes whichimages are deemed relevant to the search and which are not. In otherwords, based upon user's feedback, it specifies which images representpositive feedback and which represent negative feedback.

The feedback analyzer 154 uses the relevance feedback to narrow thesearch for relevant images. In other words, the feedback analyzer 154can progressively modify subsequent queries to maximize relevance whileminimizing the number of iterations. The analyzer 154 may strengthen therelevance of images with similar features while weakening the relevanceof images with dissimilar features.

The new relevance-feedback and query-refinement techniques describedherein may be implemented as part of the feature and semantic matcher152 and/or the feedback analyzer 154.

Accordingly, the image retrieval system seamlessly integrates semanticand feature-based relevance feedback CBIR. The system yields tremendousadvantages in terms of both retrieval accuracy and ease of use.

Image Retrieval System Architecture

FIG. 2 illustrates the image retrieval system architecture 140 in moredetail. It has a user interface (UI) 200 that accepts a selection ofexample images. The UI 200 provides navigation tools to allow the userto browse through multiple images. In the FIG. 1 network system, the UI200 can be served as an HTML document and rendered on the clientdisplay.

With UI 200, the user may select an example image from a set of sampleimages. To accomplish this, the user interface 200 initially presents aset of image categories from which the user may choose. Upon selectionof a category, the image retrieval system returns a sample set of imagespertaining to the category.

The feature and semantic matcher 152 identify images in image database142 that contain low-level features resembling the example image. Thefeature and semantic matcher 152 includes an image feature extractor 210that extracts low-level features from the candidate images in the imagedatabase 142. Such low-level features include color histogram, texture,shape, and so forth. The feature extractor 210 passes the features to animage feature matcher 212 to match the low-level features of thecandidate images with the low-level features of the example imagesubmitted by the user. Candidate images with more similar features areassigned a higher rank.

A distance calculator 214 calculates similarity distances between thefeedback images and the candidate images. See section entitled “QueryRefinement Techniques for Relevance Feedback of Image Retrieval” belowfor more information on this.

A ranking module 216 ranks the images such that the highest-rankingimages are returned to the user as the preferred results set. Theranking takes into account the closeness in features between two images.The set of highest-ranked images are returned to the user interface 200and presented to the user for consideration.

The user interface 200 allows the user to mark images as more or lessrelevant, or entirely irrelevant. The feedback analyzer 154 monitorsthis user feedback. A relevance feedback monitor 220 tracks the feedbackand performs low-level feature relevance feedback. Generally, therelevance feedback monitor 220 uses query point movement (of theimplementations of the exemplary RFCBIR) to improve the feature-basedretrieval model.

Particular implementations of the exemplary RFCBIR are described belowin more detail under the headings “Bayesian Classifier in RelevanceFeedback for Image Retrieval” and “Query Refinement Techniques forRelevance Feedback of Image Retrieval.” These implementations may beemployed by the feature and semantic matcher 152 and/or the feedbackanalyzer 154.

Bayesian Classifier in Relevance Feedback of Image Retrieval

One implementation of the exemplary RFCBIR employs a Bayesian classifierto progressively improve the relevance of the results of subsequentqueries. Bayesian classifiers are known to those of ordinary skill inthe art.

With this implementation, the probabilistic property of each image isused in the relevance feedback process. This property contains theconditional probability of each attribute value given the image and canbe updated on the fly by users' feedback. It describes a single decisionboundary through the features space.

The conventional techniques treat positive and negative examples in thefeedback (i.e., feedback images) the same in the query refinementprocess. This is shown in Formula 1 in the Background section above.This conventional approach produces less relevant results than theapproach of this implementation of the exemplary RFCBIR, describedherein. In this new approach, positive and negative feedback images aretreated differently in the query refinement process.

Bayesian Classifier

Consider vector x in R^(n) that obeys Gaussian distribution; then, theprobability density function of x is:

$\begin{matrix}{{p(x)} = {\frac{1}{( {2\pi} )^{d/2}{\Sigma }^{1/2}}{\mathbb{e}}^{{- \frac{1}{2}}{({x - ɛ})}^{T}{\sum\limits^{- 1}\;{({x - ɛ})}}}}} & (2)\end{matrix}$where x=[x₁, . . . ,x_(n)], ε=[ε(x₁), . . . , ε(x_(n))], andΣ=ε{(x−u)(x−u)^(T)}.

The following Bayesian decision boundary function is the probability ofx belonging to the i^(th) class w_(i):

$\begin{matrix}{{g_{i}(x)} = {{\lg\;{P_{i}(x)}} = {{- \frac{1}{2}}( {x - ɛ_{i}} )^{T}{\sum\limits_{i}^{- 1}\;( {x - ɛ_{i}} )}}}} & (3) \\{{{- \frac{d}{2}}\ln\; 2\pi} - {\frac{1}{2}\ln{\Sigma_{i}}} + {\ln\mspace{11mu}{P( w_{i} )}}} & \;\end{matrix}$Positive Feedback

With this implementation of the exemplary RFCBIR, the Bayesianclassifier is employed by the feedback process. Each image belongs to anunknown semantic class. In this implementation, sample-based imageretrieval is employed. That is, a user provides an example image as aquery and the image retrieval system retrieves similar images in theimage database.

It is highly unlikely that the low-level feature of the example image isjust at the distribution center of a semantic class of images. Theexemplary RFCBIR constructs a Bayesian classifier for query image by itspositive examples. The parameter of this classifier can be considered asthe real query of this image and could be updated by more feedbacks.Hence, the exemplary RFCBIR employs both a query refinement and a weightupdating process.

Each image P_(k) can be represented by a vector {right arrow over(x)}_(k)=[{right arrow over (x)}_(ki1), . . . ,{right arrow over(x)}_(km)] in the feature space where {right arrow over(x)}_(ki)=└x_(ki1), . . . , x_(kin) _(i) ┘. For each feature vector{right arrow over (x)}_(ki), there is a n_(n) _(i) ×n_(i) dimensioncovariance matrix Σ_(ki) and an n dimension mean vector ε_(ki) todescribe their query vector. n_(k) is the number of positive feedbacksto image P_(k). Since the inter-feature covariance is not considered,the diagonal matrix diag{σ_(ki)} is used instead, whereσ_(ki)(m)=Σ_(ki)(m,m). This is because the inter-feature correlationcannot be estimated accurately and reliably, especially when there arenot enough feedbacks.

This implementation of the exemplary RFCBIR handles positive feedbacksin this manner:

-   -   Feature Normalization: This puts equal emphasis on each        component. For {right arrow over (x)}_(ki) the normalized vector        is {right arrow over (x)}′_(ki)=└x_(ki1), . . . , x_(kin) _(i) ┘        , where

$x_{{ki}_{m}}^{\prime} = {{\frac{x_{k_{i_{m}}}^{''} - {ɛ( x_{k_{i_{m}}}^{''} )}}{3{\sigma( x_{k_{i_{m}}} )}}\mspace{14mu}{and}\mspace{14mu} x_{{ki}_{m}}^{''}} = {\frac{x_{{ki}_{m}}^{''} - {\min( X_{{ki}_{m}} )}}{{\max( X_{{ki}_{m}} )} - {\min( X_{{ki}_{m}} )}}.}}$

 If x_(ki) _(m) satisfies the Gaussian distribution, it is easy to provethat the probability of x′_(ki) _(m) being in the range of [−1,1] is99%.

-   -   Initialization: Initialize σ_(ki) to be null and let        ε_(ki)={right arrow over (x)}_(ki), n_(k)=1.    -   Feedback and Update Parameters: In each cycle of P_(k);s        retrieval process, suppose there is a positive example set        C_(p)={P_(p1) . . . P_(Pq)}: according to Equation 3 the        following is the resulting update procedure:

$\begin{matrix}{{\sigma_{ki}^{2} = {{n_{k}\sigma_{ki}^{2}} + \frac{n_{k}q\; ɛ_{k_{i}}^{2}2n_{k}ɛ_{k_{i}}\Sigma\; P_{Pi}}{n_{k} + q} + {\Sigma\; P_{Pi}^{2}} - \frac{( {\sum\limits^{\;}\; P_{Pi}} )^{2}}{n_{k} + q}}},} \\{{ɛ_{ki} = \frac{{n_{k} \times ɛ_{ki}} + {{sum}( C_{p} )}}{n_{k} + q}},} \\{n_{k} = {n_{k} + {q.}}}\end{matrix}$

-   -   Distance Calculation: For each image P_(i) in the database, its        distance d_(i,k) is calculated to the example image P_(k) using        Equation 3 in the retrieval after the feedback. d_(i,k)=−g_(k)        (P_(i)) That is, the similarity of each image in the database to        be refined query is determined by Equation 3 based on the        positive examples.    -   Sorting by distance if there is no negative feedback.        Negative Feedback

Conventional techniques use the same technique to handle negative andpositive feedbacks. However, with the exemplary RFCBIR, they are treateddifferently. Positive examples are usually considered to belong to thesame semantic class and there are well agreed-upon understandings. Onthe other hand, negative examples are often not semantically related.Typically, negative examples are often isolated and independent.Therefore, the inventors of this present invention recognized anadvantage to treat positive and negative examples differently.

In an implementation of the exemplary RFCBIR, the negative examples arehandled in this manner. Suppose that there is a set of negativefeedbacks, C_(N)={P_(N1) . . . P_(Nl)}, for image P_(k). For eachelement in C_(N), a ‘dibbling’ process is applied in calculated thesimilarity distance of each database images in the refined retrieval.That is, images that are near the negative examples are penalized byincreasing similarity distance d_(i,k) as defined in Equation 4 below.

With this strategy, there will be a peak in similarity distance at eachnegative example. By extensive simulation, it was determined that thefunction can be well approximated by the combination of a series ofGaussian function:d _(i,k) =d _(i,k)+Σ_(i=1) ¹(p _(P) _(nj) (P _(i))×d _(k,n) _(i) )  (4)where p_(P) _(nj) (x) is defined in Equation 2 with ε=P_(ni), Σ=I.

In this way, images in the database that are clause to the negativeexamples are pushed away from being selected into the processingretrieved image list.

Other Point Movement Strategies Employing a Bayesian Method

As motioned in the Background section above, some conventionalimplementations of point movement strategy use a Bayesian method.Specifically, these include Cox et al. and Vasconcelos/Lippman.

In these conventional works, they consider the feedback examples to thesame query to be independent with each other. They do this so that theycan use Naive Bayesian Inference to optimize the retrieval results byusing feedback examples.

In contrast to these conventional works, the exemplary RFCBIR treat allpositive examples to be closely related with each other. The exemplaryRFCBIR uses these positive examples of the same query to construct aBayesian classifier and use that classifier to represent the originalquery and try to get more accurate retrieval results.

Unlike these conventional works, the exemplary RFCBIR is incremental.The user's previous feedback information is stored in the parameters ofthe classifier and is used. This information is updated by the latterfeedback so that less iterations of feedback are necessary and highaccuracy can be achieved.

Query Refinement Techniques for Relevance Feedback of Image Retrieval

The ability for an image retrieval system to effectively use userfeedbacks is the key to providing accurate retrieval results. Theconventional method of utilizing this information is to construct theideal query vector using a weighted feature sum (WFS) of the positivefeedbacks (discussed above in the Background section).

There are several shortcomings associated with this conventionalapproach. First, the user has to provide the degree of relevanceassociated with each feedback. This step is tedious and inaccurate asthe users themselves are uncertain about the degree of relevance when itis expressed in numerical values. Furthermore, as the feedback processcycles, it is hard to guarantee the convergence of the estimated idealquery vectors. As a result, retrieval performance may not improve withincreasing numbers of feedback cycles.

Described here are implementations of the exemplary RFCBIR that employquery refinement techniques that eliminate these problems. The mainproblem of all feedback systems is how to evaluate the similaritybetween images in database and feedback images more accurately. Theseimplementations include new techniques for calculating the distance ofan image to a group of images and present our query refinement frameworkbased on them.

For the following descriptions of implementations of the exemplaryRFCBIR, assume an image database D consists of M images. In one feedbackiteration, the user provides N_(R) relevant and N_(N) irrelevant imagesrespectively. X_(i) ⁺, i=1, . . . , N_(R) is defined as the i^(th)positive feedback image, and X_(i) ⁻ ,i=1, . . . , N_(N) is defined asthe i^(th) negative feedback image.

Implementation Employing the Weighted Distance Sum (WDS) Technique

The WFS method does not work well in common image retrieval systemsbecause the weighted feature sum is not meaningful if the feedbackimages representing multiple different concepts. Hence, the similaritybetween the j^(th) image in database and the average feature will neverreflect the real distance between j and feedback images.

This implementation employs the Weighted Distance Sum (WDS) technique.This technique uses the weighted similarity distances between j^(th)image in database and feedback images as the distance between them. Thesimilarity evaluation is given by

$\begin{matrix}{{{Dis}(j)} = {\sum\limits_{i = 0}^{N_{R}}\;{w_{i}*( {1 - {{Sim}( {j,X_{i}^{+}} )}} )}}} & (5)\end{matrix}$where w_(i) is the normalized weight of each feedback images specifiedby user. Sim(j,X_(i) ⁺) is the similarity between image j and X_(i) ⁺ inlow-level features, where 0≦Sim(j,X_(i) ⁺)≦1. The larger the value is,the more similar these two images would be.Implementation Employing the Minimal Distance (MD) Technique

In many cases, the set of user's feedbacks have similar semanticmeanings, but may differ greatly in feature space. Most often, the useris only interested in an object or a region contained in the feedbackimages. The conventional method of estimation of the ideal queryparameter as the weighted sum of the feature vectors of the feedbackimages does not consider this. Instead, it simply averages allcomponents of the feature vectors by the user-assigned degree ofrelevance for each feedback images.

In each feedback iteration, users often pay attention to the semanticcontent. But those positive feedback images selected by the users may bedifferent in each low-level feature. In fact, the negative feedbackimages are always dissimilar in both semantic and low-level features.Hence, any attempt to average the distances or the features of feedbackimages is not suitable to some extent.

This implementation employs the Minimal Distance (MD) technique. Thistechnique uses the nearest neighbor method to define the distancebetween j^(th) image in database and those positive feedback images.Dis(j)=Min{(1−Sim(j,X _(i) ⁺))|i=1, . . . ,N _(R)}  (6)

From Equation 6, it can be seen that, if the feedback images have largevariance in low-level features, the most similar images to each of themare found. Therefore, any feedback image will be treated as a singlequery and the images retrieved are those similar enough to any one ofthem.

Implementation Employing the Minimal Distance Rank (MDR) Technique

An important assumption behind the MD technique is that images in thedatabase are evenly distributed in both semantic and feature space. Ifthis assumption is not valid, then the resulting retrieval favors only asubset of the positive feedback images (rather than all of such images).One solution to this problem is to consider the minimum relative rankingof each image in the database with respect to the positive feedbacks.Thus, this implementation employs the minimal distance rank (MDR)technique to overcome this situation by using relative ranks.

First, Sim(j,X_(i) ⁺), jεM is calculated, which is the similaritybetween i^(th) feedback images and all images in database. After that,this similarity is used to get the rank of images j correspond to i^(th)positive feedback image X_(i) ⁺using Equation 7.R(j,X _(i) ⁺)=Rank{(1−Sim(j,X _(i) ⁺))|jεM}  (7)where R(j,X_(i) ⁺) is the rank of image j to i^(th) positive feedbackimage X_(i) ⁺, with the smallest rank meaning the most similar.

After determining all the images' ranks to each individual positiveimages, the final rank of image j to all positive feedback images isgiven by Equation 8 based on which the system sorts the retrievalresult.R(j,X _(i) ⁺)=Min{R(j,X _(i) ⁺)|i=1, . . . , N _(R)}  (8)

Of these three query-refinement techniques described above, the MDRtechnique typically produces better results than the WDS or the MDtechniques. Thus, the MDR technique is favored.

Hybrid Implementations

Other implementations of the exemplary RFCBIR may include any reasonablecombination of the above techniques. Specifically, other implementationsmay include any reasonable combination of the Bayesian classifiertechnique, the WDS technique, the MD technique, or the MDR technique.Relevance Feedback Integration

An exemplary image retrieval system framework may be formed by combiningthe MDR and MD techniques. Given a group of positive feedback imagesX_(i) ⁺,i=1, . . . ,N_(R) and negative feedback images X_(i) ⁻,i=1, . .. , N_(N) in certain feedback iteration, the exemplary RFCBIRfirst usethe MD method to calculate the distance between image j in database andall negative feedback images.Dis(j)=Min{(1−Sim(j,X _(i) ⁻))|i=1, . . . ,N _(N)}  (9)

The exemplary RFCBIRuses X_(j,k) ⁻,k=1, . . . , N_(N) to indicate thenegative feedback image corresponding to image j with Eq. (9). Theexemplary RFCBIRthen uses Eq. (10) to calculate the distance between thej^(th) image and positive feedback image X_(i) ⁺.Dis(j,X _(i) ⁺)=2.0−Sim(j,X _(i) ⁺)*{Dis(j)+Sim(X _(i) ⁺ , X _(j,k)⁻)}  (10)

In equation 10, The exemplary RFCBIRintegrates both positive andnegative similarity to get an overall distance of image j in the currentfeedback iteration. The exemplary RFCBIRmultiply similarity Sim(X_(i) ⁺,X_(j,k) ⁻) with Sim(j,X_(i) ⁺) to indicate that, the more similar X_(i)⁺ and X_(j,k) ⁻ is, the more weight should be add to the similaritySim(j,X_(i) ⁺). It is quite often that the user marks two images whichare very similar in low-level features as positive and negativerespectively. This strategy can prevent most of images that are bothsimilar to positive and negative images from being ranked too bad.

The exemplary RFCBIRuses the distance in Eq. (10) to get the rank ofimage j in database to each positive feedback image X_(i) ⁺, as definedin Eq. (11). After the exemplary RFCBIRgets every image's rank to allpositive feedback images, the exemplary RFCBIRuses MDR method defined inEq. (8) to find the minimal rank of image j and get the final retrievalresults.R(j,X _(i) ⁺)=Rank{Dis(j,X _(i) ⁺)|jεM}  (11)Methodological Implementation of the Exemplary RFCBIR

FIGS. 3 and 4 show methodological implementations of the exemplaryRFCBIR performed by the RFCBIR system (or some portion thereof). Thesemethodological implementations may be performed in software, hardware,or a combination thereof.

FIG. 3 shows the retrieval process of the exemplary RFCBIR. At 302, asample image is inputted into the RFCBIR system. For example, a user amay select one or more images displayed by UI 200 of system 140 of FIG.2.

At 304, the user is presented with retrieval results by distance sortingbased upon a distance calculation (in accordance with equations such asequation 6 of the MD technique or equation 5 of the WDS technique). Forthe distance calculation here, any standard type of of distance matrix(e.g., measurement) may be employed. For example, it may be Mahalanobisdistance, Euclidian distance, etc. At 306, the positive and negativefeedback examples are inputted based upon the results.

At 308, the positive examples are used to update the parameters ofBayesian classifier and the ‘dibbling’ process is performed according tothe negative examples. Next, at 310, the retrieval results are updatedand presented to the user. At 312, the user decides if the user issatisfied with the relevancy of the retrieval results. If so, then theuser provides no feedback and the process ends at 314. If not, then theuser provides feedback. The process returns to block 306 and blocks306–312 are repeated based upon the new user feedback. This loopcontinues until the user provides no feedback.

FIG. 4 shows the negative feedback process of the exemplary RFCBIR. At402, it is determined whether the sample image has changed. If not, thenthe process skips block 404 and proceeds to block 406 where the newnegative examples are inserted in the list list (C_(n)). If the sampleimage has changed, then the negative example list (C_(n)) is initiatedat 404. Then the process proceeds to block 406.

At 408, start examining the first image of the database (such asdatabase 142 of FIGS. 1 and 2). At 410, determine if this is the lastimage in the database. If so, then the process ends at 418. Of course,if there is only one image in the database, then no sorting isnecessary.

If the image is not last in the database, then, at 412, update thedistance metric of that image by using equation 4 of Negative Feedbacktechnique. At 414, move to the next image in the database and theprocess returns to decision block 410 where it is determined whetherthis image is the last. If it is, then the process jumps to block 414where the images of the database are sorted according to their distance.

Data Flow of a Methodological Implementation of the Exemplary RFCBIR

FIG. 5 shows a data flow of a methodological implementation of theexemplary RFCBIR performed by the RFCBIR system (or some portionthereof).

FIG. 5 shows the data flow of the query refinement process of theexemplary RFCBIR. At 502, the user provides both positive and negativefeedback images. Blocks 504 through 518 form a loop that is repeated foreach image (i) in the image database. Similarly, blocks 506 through 514form a loop that is repeated for each positive feedback image.

At 508, the MD technique is employed to get the distance between theimage being currently examined (j) and negative feedback images. At 510,using equation 10 of Relevance Feedback Integration, the distancesbetween image j and current positive feedback image (X_(i) ⁺) iscalculated. At 512, using equation 11 of Relevance Feedback Integration,the rank of image j to the current feedback images determined. At 514,get the rank of image j to each positive feedback image X_(i) ⁺ andreturn to block 506 if more positive feedback images exist.

At 516, use the MDR technique to get the rank of image j to all feedbackimages. At 518, get the last rank of each image j in the database andreturn to block 504 if more images exist in the database. At 520, thefeedback retrieval results are presented to the user with the imagesdisplayed in order of rank.

Exemplary Computing System and Environment

FIG. 6 illustrates an example of a suitable computing environment 900within which an exemplary RFCBIR, as described herein, may beimplemented (either fully or partially). The computing environment 900may be utilized in the computer and network architectures describedherein.

The exemplary computing environment 900 is only one example of acomputing environment and is not intended to suggest any limitation asto the scope of use or functionality of the computer and networkarchitectures. Neither should the computing environment 900 beinterpreted as having any dependency or requirement relating to any oneor combination of components illustrated in the exemplary computingenvironment 900.

The exemplary RFCBIR may be implemented with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well known computing systems, environments,and/or configurations that may be suitable for use include, but are notlimited to, personal computers, server computers, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

The exemplary RFCBIR may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theexemplary RFCBIR may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

The computing environment 900 includes a general-purpose computingdevice in the form of a computer 902. The components of computer 902 caninclude, by are not limited to, one or more processors or processingunits 904, a system memory 906, and a system bus 908 that couplesvarious system components including the processor 904 to the systemmemory 906.

The system bus 908 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, sucharchitectures can include an Industry Standard Architecture (ISA) bus, aMicro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, aVideo Electronics Standards Association (VESA) local bus, and aPeripheral Component Interconnects (PCI) bus also known as a Mezzaninebus.

Computer 902 typically includes a variety of computer readable media.Such media can be any available media that is accessible by computer 902and includes both volatile and non-volatile media, removable andnon-removable media.

The system memory 906 includes computer readable media in the form ofvolatile memory, such as random access memory (RAM) 910, and/ornon-volatile memory, such as read only memory (ROM) 912. A basicinput/output system (BIOS) 914, containing the basic routines that helpto transfer information between elements within computer 902, such asduring start-up, is stored in ROM 912. RAM 910 typically contains dataand/or program modules that are immediately accessible to and/orpresently operated on by the processing unit 904.

Computer 902 may also include other removable/non-removable,volatile/non-volatile computer storage media. By way of example, FIG. 6illustrates a hard disk drive 916 for reading from and writing to anon-removable, non-volatile magnetic media (not shown), a magnetic diskdrive 918 for reading from and writing to a removable, non-volatilemagnetic disk 920 (e.g., a “floppy disk”), and an optical disk drive 922for reading from and/or writing to a removable, non-volatile opticaldisk 924 such as a CD-ROM, DVD-ROM, or other optical media. The harddisk drive 916, magnetic disk drive 918, and optical disk drive 922 areeach connected to the system bus 908 by one or more data mediainterfaces 926. Alternatively, the hard disk drive 916, magnetic diskdrive 918, and optical disk drive 922 can be connected to the system bus908 by one or more interfaces (not shown).

The disk drives and their associated computer-readable media providenon-volatile storage of computer readable instructions, data structures,program modules, and other data for computer 902. Although the exampleillustrates a hard disk 916, a removable magnetic disk 920, and aremovable optical disk 924, it is to be appreciated that other types ofcomputer readable media which can store data that is accessible by acomputer, such as magnetic cassettes or other magnetic storage devices,flash memory cards, CD-ROM, digital versatile disks (DVD) or otheroptical storage, random access memories (RAM), read only memories (ROM),electrically erasable programmable read-only memory (EEPROM), and thelike, can also be utilized to implement the exemplary computing systemand environment.

Any number of program modules can be stored on the hard disk 916,magnetic disk 920, optical disk 924, ROM 912, and/or RAM 910, includingby way of example, an operating system 926, one or more applicationprograms 928, other program modules 930, and program data 932. Each ofsuch operating system 926, one or more application programs 928, otherprogram modules 930, and program data 932 (or some combination thereof)may include an embodiment of relevance feedback sub-system and feedbackanalyzer.

A user can enter commands and information into computer 902 via inputdevices such as a keyboard 934 and a pointing device 936 (e.g., a“mouse”). Other input devices 938 (not shown specifically) may include amicrophone, joystick, game pad, satellite dish, serial port, scanner,and/or the like. These and other input devices are connected to theprocessing unit 904 via input/output interfaces 940 that are coupled tothe system bus 908, but may be connected by other interface and busstructures, such as a parallel port, game port, or a universal serialbus (USB).

A monitor 942 or other type of display device can also be connected tothe system bus 908 via an interface, such as a video adapter 944. Inaddition to the monitor 942, other output peripheral devices can includecomponents such as speakers (not shown) and a printer 946 which can beconnected to computer 902 via the input/output interfaces 940.

Computer 902 can operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computingdevice 948. By way of example, the remote computing device 948 can be apersonal computer, portable computer, a server, a router, a networkcomputer, a peer device or other common network node, and the like. Theremote computing device 948 is illustrated as a portable computer thatcan include many or all of the elements and features described hereinrelative to computer 902.

Logical connections between computer 902 and the remote computer 948 aredepicted as a local area network (LAN) 950 and a general wide areanetwork (WAN) 952. Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets, and the Internet.

When implemented in a LAN networking environment, the computer 902 isconnected to a local network 950 via a network interface or adapter 954.When implemented in a WAN networking environment, the computer 902typically includes a modem 956 or other means for establishingcommunications over the wide network 952. The modem 956, which can beinternal or external to computer 902, can be connected to the system bus908 via the input/output interfaces 940 or other appropriate mechanisms.It is to be appreciated that the illustrated network connections areexemplary and that other means of establishing communication link(s)between the computers 902 and 948 can be employed.

In a networked environment, such as that illustrated with computingenvironment 900, program modules depicted relative to the computer 902,or portions thereof, may be stored in a remote memory storage device. Byway of example, remote application programs 958 reside on a memorydevice of remote computer 948. For purposes of illustration, applicationprograms and other executable program components such as the operatingsystem are illustrated herein as discrete blocks, although it isrecognized that such programs and components reside at various times indifferent storage components of the computing device 902, and areexecuted by the data processor(s) of the computer.

Computer-Executable Instructions

An implementation of an exemplary RFCBIR may be described in the generalcontext of computer-executable instructions, such as program modules,executed by one or more computers or other devices. Generally, programmodules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Exemplary Operating Environment

FIG. 6 illustrates an example of a suitable operating environment 900 inwhich an exemplary RFCBIR may be implemented. Specifically, theexemplary RFCBIR(s) described herein may be implemented (wholly or inpart) by any program modules 928–930 and/or operating system 926 in FIG.6 or a portion thereof.

The operating environment is only an example of a suitable operatingenvironment and is not intended to suggest any limitation as to thescope or use of functionality of the exemplary RFCBIR(s) describedherein. Other well known computing systems, environments, and/orconfigurations that are suitable for use include, but are not limitedto, personal computers (PCs), server computers, hand-held or laptopdevices, multiprocessor systems, microprocessor-based systems,programmable consumer electronics, wireless phones and equipments,general-and special-purpose appliances, application-specific integratedcircuits (ASICs), network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

Computer Readable Media

An implementation of an exemplary RFCBIR may be stored on or transmittedacross some form of computer readable media. Computer readable media canbe any available media that can be accessed by a computer. By way ofexample, and not limitation, computer readable media may comprise“computer storage media” and “communications media.”

“Computer storage media” include volatile and non-volatile, removableand non-removable media implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules, or other data. Computer storage mediaincludes, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by acomputer.

“Communication media” typically embodies computer readable instructions,data structures, program modules, or other data in a modulated datasignal, such as carrier wave or other transport mechanism. Communicationmedia also includes any information delivery media.

The term “modulated data signal” means a signal that has one or more ofits characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, and other wireless media. Combinations of any of the above arealso included within the scope of computer readable media.

Conclusion

Although the invention has been described in language specific tostructural features and/or methodological steps, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or steps described. Rather, thespecific features and steps are disclosed as preferred forms ofimplementing the claimed invention.

1. One or more computer-readable media having computer-executableinstructions that, when executed by a computer, perform a method forimproving iterative results of Content-Based Image Retrieval (CBIR)using relevance feedback, the method comprising: obtaining a set ofpositive feedback images and a set of negative feedback images viarelevance feedback, the set of positive feedback images are those imagesdeemed semantically relevant and the set of negative feedback images arethose deemed semantically less relevant; constructing a Bayesianclassifier of a positive feedback image by positive candidate images;within a feature space, moving a positive candidate image towards theset of positive feedback images by adjusting distance metrics of thepositive candidate image, the positive candidate image having similarlow-level features as those of the set of positive feedback images,wherein the adjusting of distance metrics of the positive candidateimage employs this evaluation:${{{Dis}(j)} = {\sum\limits_{i = 0}^{N_{R}}\;{w_{i}*( {1 - {{Sim}( {j,X_{i}^{+}} )}} )}}};$where j is the positive candidate image; N_(R) is a number of images inthe set of positive feedback images; X_(i) ⁺, i=1, . . . , N_(R) isdefined as the i^(th) image; w_(i) is the normalized weight of theimages in the set of positive feedback images; within a feature space,distancing a negative candidate image from the set of positive feedbackimages by adjusting distance metrics of the negative candidate image,the negative candidate image having similar low-level features as thoseof the set of negative feedback images.
 2. One or more media as recitedin claim 1 further comprising employing a Bayesian decision boundaryfunction to determine the probability of an image being a positivecandidate image.
 3. One or more media as recited in claim 1, wherein themoving further comprises: normalizing features of an image within afeature space; initializing parameters of the classifier; updating theparameters using the features of the new positive feedback images;calculating distances based upon a Bayesian decision boundary function;sorting images based upon calculated distances, wherein the sort isperformed as if no negative feedback images exist.
 4. One or morecomputer-readable media having computer-executable instructions that,when executed by a computer, perform a method for improving theevaluation of the similarity of images based upon image features, themethod comprising defining a subject image to be similar to a set ofexample images because it is within a minimal distance of the set ofexample images within a feature space, wherein the distance between thesubject image and the set of example images is calculated based uponthis evaluation:${{{Dis}(j)} = {\sum\limits_{i = 0}^{N_{R}}\;{w_{i}*( {1 - {{Sim}( {j,X_{i}^{+}} )}} )}}};$where j is the subject image; N_(R) is a number of images in the set ofexample images; X_(i) ⁺, i=1, . . . , N_(R) is defined as the i^(th)image; w_(i) is the normalized weight of the images in the set ofexample images; repeating the defining for each example image in a setof example images and for each subject image in a set of subject images.5. One or more media as recited in claim 4 further comprising rankingsimilarly of each subject image in the set of subject images to eachexample image in the set of example images.
 6. A system for improvingiterative results of Content-Based Image Retrieval (CBIR) usingrelevance feedback, the system comprising: a relevance feedback meansfor obtaining a set of positive feedback images and a set of negativefeedback images via relevance feedback, the set of positive feedbackimages are those images deemed semantically relevant and the set ofnegative feedback images are those deemed semantically less relevant; afeedback analyzing means for performing functions within a featurespace, the functions comprising: moving a positive candidate imagetowards the set of positive feedback images by adjusting distancemetrics of the positive candidate image, the positive candidate imagehaving similar low-level features as those of the set of positivefeedback images, wherein the adjusting of distance metrics of thepositive candidate image employs this evaluation:${{{Dis}(j)} = {\sum\limits_{i = 0}^{N_{R}}\;{w_{i}*( {1 - {{Sim}( {j,X_{i}^{+}} )}} )}}};$where j is the positive candidate image; N_(R) is a number of images inthe set of positive feedback images; X_(i) ⁺, i=1, . . . , N_(R) isdefined as the i^(th) image; w_(i) is the normalized weight of theimages in the set of positive feedback images; distancing a negativecandidate image from the set of positive feedback images by adjustingdistance metrics of the negative candidate image, the negative candidateimage having similar low-level features as those of the set of negativefeedback images.
 7. A system as recited in claim 6, wherein the feedbackanalyzing means is also for constructing a Bayesian classifier of apositive feedback image by positive candidate images.
 8. A system asrecited in claim 6, wherein the feedback analyzing means is also fordefining a subject image to be similar to an example image because it iswithin a minimal distance of the example image within a feature space.